Hidden Markov Models (HMMs)
Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression.
... We will briefly review Forward-Backward Gibbs sampling (FBG) for Bayesian Hidden Markov Models, and its acceleration through compression of the data into blocks. By first considering the case of ...
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Effective identification of conserved pathways in biological networks using hidden Markov models.
... algorithms can effectively find conserved substructures with relatively small sizes, but many of them suffer from high computational complexity that makes it difficult to find larger substructures. Furthermore, many ...
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Hidden Markov models applied to a subsequence of the Xylella fastidiosa
... that hidden Markov Models (HMM) exemplify this notion very well since although these models are not new, we believe that molecular biolo- gists are not aware of the possibilities that these ...
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Predicting market direction with hidden Markov models
... By assigning a probability matrix to both the eleven different mood levels and the three different states, HMMs are able to output the optimal state sequence the game outcomes by maximiz[r] ...
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Isolated Malay Digit Recognition Using Pattern Recognition Fusion of Dynamic Time Warping and Hidden Markov Models
... Hidden Markov Model (HMM): HMM is typically an interconnected group of states that are assumed to emit a new feature vector for each frame according to an emission probability density function associated ...
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Subject-driven supervised and unsupervised Hidden Markov Models for heart sound segmentation in real noisy environments
... statistical models known as HSMM. Recently, these models have been proposed as an alternative to the typical and standard HMM and several studies have been made in the topic ...
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Use of Hidden Markov Mobility Model for Location Based Services
... These days people prefer to use portable and wireless devices such as laptops, mobile phones, They are connected through satellites. As user moves from one point to other, task of updating stored information becomes ...
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Automatic speech recognition: a study and performance evaluation on neural networks and hidden markov models
... training. In [2] this same segmentation and number of Table 3 is relative to the neural models and shows the features restriction was observed for the HMM best re[r] ...
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Hidden Markov models for credit risk
... Binomial Hidden Markov Model, considering only two dierent risk categories to fully explain default occurrence, and approximating it to a Poisson Hidden Markov Model, with all the ...
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Mapping dynamic environments using Markov random field models
... Abstract—This paper focuses on dynamic environments for mobile robots and proposes a new mapping method combining hidden Markov models (HMMs) and Markov random fields (MRFs). Grid cells are ...
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Automatic speech recognition: a comparative evaluation between neural networks and hidden markov models
... We evaluated the chosen neural models, the MLP and the Radial Basis performance of all models using two different test sets nets, and some Continuous Density Hidden[r] ...
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HIDDEN MARKOV MODELS WITH COVARIATES FOR ANALYSIS OF DEFECTIVE INDUSTRIAL MACHINE PARTS
... (Sirima and Pokorny, 2014) was proposed. The major research question is whether the machine part error correction and the amount of time spent on error correction reduce the probability of subsequently entering the ...
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Hidden Markov Models and the Baum-Welch Algor.
... At this point Leonard Baum and I found that we had both been working independently on Hidden Markov Chains and had both come up with essentially the same calculation for a posteriori probabilities of ...
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A Novel Approach for Inflation Analysis Using Hidden Markov Model
... A Hidden Markov Model (HMM) [12] is a finite state machine which has some fixed number of ...observations. Hidden Markov models were introduced in the beginning of the 1970’s as a tool ...
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Clustering financial time series: new insights from an extended hidden Markov model
... as hidden Markov model, underlying each ...few. Hidden Markov models and regime-switching models as discrete state models can also be connected to stochastic volatility ...
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On the representability of complete genomes by multiple competing finite-context (Markov) models.
... k. Markov modeling has long been applied to DNA sequences, for example to find gene- coding ...then, Markov and hidden Markov models have been extensively used to describe the gene ...
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EXTENSION OF HIDDEN MARKOV MODEL FOR RECOGNIZING LARGE VOCABULARY OF SIGN LANGUAGE
... dea of this approach comes from many researchers in li into French Sign Language (FSL), most of which conc features. Each hand had their own features such as the ing the manual and non-manual components within ction in ...
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Diversity in Teams: Source of Conflict or Source of Creativity? Investigating Hidden Mental Models
... mental models acquired within this process. These mental models are not only individually shaped but are also collectively shared constructions of ...
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Classification of Flying Insects with high performance using improved DTW algorithm based on hidden Markov model
... Insects play significant role in the human life. And insects pollinate major food crops consumed in the world. Insect pests consume and destroy major crops in the world. Hence to have control over the disease and pests, ...
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Identifying Currency Bubbles using Markov-Switching Models: The Latin American case
... other models were proposed, where both positive and negative bubbles are permitted and the probability of collapsing depends on the size of the bubble 2 ...
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